A wide variety of systems can be descried as multi-variable systems. For example systems such as building heating, ventilation and air-conditioning (HVAC) systems, refrigeration systems, energy distribution systems, automated lighting systems, security/alarm systems, smoke/fire detection systems, and gas detection systems.
Multi-variable systems such as these commonly fail to satisfy performance expectations envisioned at design time. Such failings can be due to a number of factors, such as problems caused by improper installation, inadequate maintenance, or equipment failure. These problems, or “faults,” can include mechanical failures, sensor failures/errors, control problems, design errors, and inappropriate operator intervention. Due to the complexity of such multi-variable systems, it is relatively common for faults to go unnoticed for extended periods of time, and only identified when they become significant enough to cause complete equipment failure, excessive power consumption, and/or sufficient performance degradation to trigger user complaints.
By way of one example, faults in HVAC systems may be caused be a variety of factors. Mechanical faults can include stuck, broken, or leaking valves, dampers, or actuators, fouled heat exchangers, or other damaged/inoperative components. Control problems ran relate to failed or drifting sensors, poor feedback loop tuning or incorrect sequencing logic.
A variety of fault detection and diagnostic (FDD) techniques for multi-variable systems are known, and their use provides for a number of benefits. By detecting and acting on faults in multi-variable systems significant energy savings can be realised. Additionally, if minor faults are detected before becoming major problems, the useful service life of equipment can be extended, maintenance costs can be reduced, and repairs can be scheduled when convenient (avoiding downtime and overtime work).
Further, and again using a HVAC system by way of example, detecting faults allows for better control of temperature, humidity, and ventilation of occupied spaces. This, in turn, can improve employee productivity, guest/customer comfort, and/or product quality control.
Many current fault detection techniques for multi-variable systems are rule-based. The fault detection system integrates and interprets incoming data in accordance with a pre-determined set of rules, produces a risk profile, and autonomously initiates a response to a breach of these rules. Rule-based systems are, however, limited insofar as they are very specifically derived for/tailored to a particular system and are very difficult to update, change, or adapt to a different system. Additionally, rule-based systems typically fail miserably if conditions beyond the boundaries of the knowledge incorporated in them are encountered.
Although less common, another class of fault detection techniques used for multi-variable systems are qualitative model-based systems. Qualitative model-based systems use analytical mathematical models to identify faults. As with rule-based systems, however, qualitative model-based systems have a number of limitations. For example, qualitative model-based systems are generally complex and computationally intensive, and a large amount of skilled work is required to develop a model for a particular system. Also, in order to create a usable model many inputs are required to describe the system being modelled, and the values of some of the required inputs may not be readily available.
In addition to the above limitations, most multi-variable systems are installed in different buildings/environments. This generally means that rules or analytical models developed for a particular system cannot be easily applied to an alternative system. As such, the difficult process of determining and setting rules or generating analytical mathematical models must be tailored to each individual building/environment. In addition, the task of setting the thresholds used by such systems to raise alarms is involved, and prone to producing false alarms. Also, building conditions such as structure of the internal architecture design and even external factors (such as shading and the growth of plant life) often change after the system installation/initialisation of a fault detection system, which can require rules/models that were originally appropriate to be revisited and updated.
It would be desirable to provide a fault detection system and/or method which overcomes or ameliorates one or more of the limitations of existing fault detection systems/methods. In the alternative, it would be desirable to provide a useful alternative to existing fault detection systems and methods.
Reference to any prior art in the specification is not, and should not be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general knowledge in Australia or any other jurisdiction or that this prior art could reasonably be expected to be ascertained, understood and regarded as relevant by a person skilled in the art.